Comparison between different features and predictive models to find proteins similar to C-peptideC-펩타이드 유사 단백질 발견을 위한 이종 특성 및 예측 모델 간의 비교에 관한 연구

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The C-peptide is a short protein that connects the A and B chains’ in the insulin prohrmone. Since its de-scription in 1967, it has been historically thought to be an inert and biologically non-active peptide. That is, no physiological roles or functions were attributed to it other than connecting A and B chains’ and proper folding of mature insulin. An increasing body of experimental evidence has challenged this view and purports the notion that C-peptide is bioactive, evidenced by observed signaling characteristics from in vitro experimental studies. The most pronounced is the ameliorated effect it has on diabetes induced renal and nerve dysfunction. Accord-ingly, the past decade has witnessed a renewal in C-peptide research following these findings aimed at providing a complete physiological characterization of the peptide. The most prominent of these include finding more functions and determining whether a receptor exits or not. In this research we provide the first attempt to computationally address thisendeavor. We performed an investigative study of C-peptide that spanned 75 organisms, to determine intrinsic features that can aide in detecting similar proteins. Features were extracted and 5 different encodings were used in unison with predictive models (Support vector machines, na"ive Bayes and others). A comparative study between these models was performed to determine the most suitable features that should be highly considered for the computational characterization of C-peptide.
Advisors
Choi, Ho-Jinresearcher최호진
Description
한국과학기술원 : 전산학과,
Publisher
한국과학기술원
Issue Date
2013
Identifier
515113/325007  / 020104472
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학과, 2013.2, [ iv, 49 p. ]

Keywords

C-peptide; physiochemical properties; features encoding; C-펩타이드; 이화학적 특성; 피쳐 인코딩; 예측 모델; predictive models

URI
http://hdl.handle.net/10203/180456
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=515113&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
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